Schizophrenia is a common, severe, highly heritable psychotic disorder for which biological insights and etiological knowledge-based treatments have yet to be achieved. The vast majority of patients suffering from schizophrenia remains ill after the initial episode, suffering from chronic and severely incapacitating symptoms, and are unable to work. Genome-wide association studies (GWAS) have been successful in uncovering individual common susceptibility loci reproducibly associated with schizophrenia. However, identifying the underlying causal variants, risk genes, and etiological gene networks have proven difficult for schizophrenia, like for most other complex disorders. It is likely that many risk variants in these loci are regulatory in nature. Here, we propose to fill the gap in our biological understanding of schizophrenia etiology through an integrative genomics approach based on SNP genotype and RNAseq data from a large collection of lymphoblastoid cell lines (LCLs) derived from the Molecular Genetics of Schizophrenia case-control sample, which includes rich demographic and psychiatric information. Expression signatures will be generated at baseline and after a perturbation with dopamine, predicated on the hypothesis that cell perturbation using this pharmacologically relevant agent will reveal etiologically relevant genes which are undetectable in the unperturbed (baseline) state. We present evidence of two supporting facts: (1) Signals from expression analysis of LCLs and GWAS results converge at the major histocompatibility complex region on chromosome 6. (2) Dopamine stimulation strongly regulates the expression of many genes located in genome-wide significant GWAS loci, and in copy number variants associated with schizophrenia. Our study will capitalize on an ongoing experiment (RC2MH90030) of unstimulated (baseline) genome-wide expression profiles of LCLs from the same sample. We will identify dopamine-responsive transcripts associated with schizophrenia, analyze the underlying regulatory DNA variants (i.e., expression quantitative trait nucleotides, eQTNs), and assess the association of eQTNs with schizophrenia. We will then perform validation testing of LCL findings in neural tissues, and will functionally characterize a set of most important eQTNs. The proposed study is expected to identify new loci influencing schizophrenia risk, reveal the causal genes in already identified GWAS loci, and shed light on the underlying etiological mechanisms by establishing a connection to the mechanisms of action of antipsychotics, spearheading clinical applications in the field for diagnostic classification and treatment.